@Book{wekaDataMining,
author = {Ian H.Witten,Eibe Frank,Mark A.Hall},
title = {Data Mining: Practical Machine Learning Tools and Technology},
edition = {3rd},
publisher = {Morgen Kaufmann},
year = {2011},
}
@Misc{IndoorPPT,
author = {Prof. Dr. C. Linnhoff-Popien},
title = {Indoor Positioning:WLAN Positioning},
howpublished = {PPT},
year = {2013},
}
@Article{IEEEWiFi2013,
author = {Igor Bisio,Fabio Lavagetto, Mario Marchese,Andrea Sciarrone},
title = {GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs},
journal = {IEEE TRANSACTIONS ON MULTIMEDIA},
year = {2013},
OPTvolume = {15},
OPTnumber = {4},
OPTmonth = {June},
}
@Article{TV,
author = {M. Rabinowitz and J. J. Spilker},
title = {A new positioning system using television synchronization signals},
journal = {IEEE Trans. Broadcasting},
year = {2005},
OPTvolume = {51},
OPTnumber = {1},
OPTmonth = {May},
}
@TechReport{FMFP,
author = {Yin Chen, Dimitrios Lymberopoulos, Jie Li,BodhiPriyantha},
title = {FM-based Indoor Localization},
institution = {Johns Hopkins University,Microsoft research},
year = {2012},
OPTkey = {FM, localization, mobile systems, fingerprinting, wireless},
OPTaddress = { Low Wood Bay, Lake District, UK},
OPTmonth = {June},
OPTnote = {MobiSys},
}
@TechReport{LIFS,
author = {Zheng Yang, Chenshu Wu, and Yunhao Liu},
title = {Locating in Fingerprint Space: Wireless Indoor
Localization with Little Human Intervention},
institution = {School of Software and TNList, Tsinghua University},
year = {2012},
OPTkey = {Indoor Localization, Floor Plan, RSS Fingerprint, Smartphones, Site Survey},
OPTaddress = { Istanbul, Turkey},
OPTmonth = {August},
OPTnote = {MobiCom},
}
@InProceedings{WifiAndroid,
author = {Beom-Ju Shin, Kwang-Won Lee, Sun-Ho Choi, Joo-Yeon Kim, Woo Jin Lee, and Hyung Seok Kim},
title = {Indoor WiFi Positioning System for Android-based Smartphone},
booktitle = {Information and Communication Technology Convergence (ICTC), 2010 International Conference on},
OPTkey = {Wi-Fi, Wi-Fi Positioning System, Android,smartphone},
OPTpages = {319-320},
OPTyear = {2010},
OPTaddress = {Jeju, South Korea},
OPTmonth = {Nov},
}
@online{skyhook,
author = {Skyhook},
title = {The World Leader in Location Information},
year = {2011},
url = {http://www.skyhookwireless.com},
OPTsubtitle = {Context and Intelligence},
}
@article{raudysSampleSizeAlgorithms,
 author = {Raudys, Sarunas and Pikelis, Vitalijus},
 title = {On Dimensionality, Sample Size, Classification Error, and Complexity of Classification Algorithm in Pattern Recognition},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 volume = {2},
 number = {3},
 month = {Mar},
 year = {1980},
 issn = {0162-8828},
 pages = {242--252},
 numpages = {11},
 url = {http://dx.doi.org/10.1109/TPAMI.1980.4767011},
 doi = {10.1109/TPAMI.1980.4767011},
 acmid = {2052899},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {Classification error, dimensionality, discriminant functions, pattern recognition, sample size},
} 

@article{raudysSampleSize,
 author = {Raudys, Sarunas J. and Jain, Anil K.},
 title = {Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 volume = {13},
 number = {3},
 month = {Mar},
 year = {1991},
 issn = {0162-8828},
 pages = {252--264},
 numpages = {13},
 url = {http://dx.doi.org/10.1109/34.75512},
 doi = {10.1109/34.75512},
 acmid = {105498},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {classifiers, error estimation, error rates, feature selection, learning, pattern recognition, sample size effects, statistical analysis, statistical pattern recognition},
} 
@article{decisionSampleSize,
    author = {Bartlett, James E. and Kotrlik, Joe W. and Higgins, Chadwick C.},
    citeulike-article-id = {8878157},
    journal = {Information Technology, Learning and Performance},
    keywords = {statistics},
    number = {1},
    pages = {43--50},
    posted-at = {2011-02-23 15:19:48},
    priority = {0},
    title = {{Organizational Research: Determining Appropriate Sample Size in Survey Research}},
    volume = {19},
    year = {2001}
}
@MISC{sizeformular,
    author = {Carlos E. Thomaz and Duncan F. Gillies},
    title = {"Small Sample Size": A Methodological Problem in Bayes Plug-in Classifier for Image Recognition},
    year = {}
}

@TECHREPORT{ballTree,
    author = {Stephen M. Omohundro},
    title = {Five Balltree Construction Algorithms},
    institution = {},
    year = {1989}
}
@article{kdTree,
 author = {Bentley, Jon Louis},
 title = {Multidimensional binary search trees used for associative searching},
 journal = {Commun. ACM},
 volume = {18},
 number = {9},
 month = sep,
 year = {1975},
 issn = {0001-0782},
 pages = {509--517},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/361002.361007},
 doi = {10.1145/361002.361007},
 acmid = {361007},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {associative retrieval, attribute, binary search trees, binary tree insertion, information retrieval system, intersection queries, key, nearest neighbor queries, partial match queries},
} 
@MastersThesis{coverTree,
author = {David Marshall},
title = {Nearest Neighbour Searching in High Dimensional Metric Space},
school = {Australian National University},
year = {2006},
OPTmonth = {June},
}

@TECHREPORT{SMO,
    author = {John C. Platt},
    title = {Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines},
    institution = {ADVANCES IN KERNEL METHODS - SUPPORT VECTOR LEARNING},
    year = {1998}
}
@INPROCEEDINGS{randomForest,
    author = {Leo Breiman Statistics and Leo Breiman},
    title = {Random Forests},
    booktitle = {Machine Learning},
    year = {2001},
    pages = {5--32}
}
@INPROCEEDINGS{c45,
    author = {J. R. Quinlan},
    title = {Bagging, Boosting, and C4.5},
    booktitle = {In Proceedings of the Thirteenth National Conference on Artificial Intelligence},
    year = {1996},
    pages = {725--730},
    publisher = {AAAI Press}
}
@ARTICLE{decisionTree,
    author = {J. R. Quinlan},
    title = {Induction of Decision Trees},
    journal = {Mach. Learn},
    year = {1986},
    pages = {81--106}
}
@misc{rf_overfit,
    abstract = {{Breiman (2001a,b) has recently developed an ensemble classification and regression approach that displayed outstanding performance with regard prediction error on a suite of benchmark datasets. As the base constituents of the ensemble are tree-structured predictors, and since each of these is constructed using an injection of randomness, the method is called `random forests'. That the exceptional performance is attained with seemingly only a single tuning parameter, to which sensitivity is minimal, makes the methodology all the more remarkable. The individual trees comprising the forest are all grown to maximal depth. While this helps with regard bias, there is the familiar tradeoff with variance. However, these variability concerns were potentially obscured because of an interesting feature of those benchmarking datasets extracted from the UCI machine learning repository for testing: all these datasets are hard to overfit using tree-structured methods. This raises issues about the scope of the repository. With this as motivation, and coupled with experience from boosting methods, we revisit the formulation of random forests and investigate prediction performance on real-world and simulated datasets for which maximally sized trees do overfit. These explorations reveal that gains can be realized by additional tuning to regulate tree size via limiting the number of splits and/or the size of nodes for which splitting is allowed. Nonetheless, even in these settings, good performance for random forests can be attained by using larger (than default) primary tuning parameter values.}},
    author = {Segal, Mark R.},
    citeulike-article-id = {11837490},
    citeulike-linkout-0 = {http://www.escholarship.org/uc/item/35x3v9t4},
    keywords = {benchmark, machine-learning, overfit, prediction-error, random-forest},
    posted-at = {2012-12-10 13:18:02},
    priority = {5},
    title = {{Machine Learning Benchmarks and Random Forest Regression}},
    url = {http://www.escholarship.org/uc/item/35x3v9t4},
    year = {2004}
}

@Unpublished{pattern,
author = {Prof. Dr.-Ing. Rolf-Rainer Grigat},
title = {Pattern Recognition},
OPTmonth = {April},
OPTyear = {2012},
institution = {Technische Universität Hamburg-Harburg Vision Systems}
}








